视觉搜索的非短视方法

Julia Vogel, Kevin P. Murphy
{"title":"视觉搜索的非短视方法","authors":"Julia Vogel, Kevin P. Murphy","doi":"10.1109/CRV.2007.5","DOIUrl":null,"url":null,"abstract":"We show how a greedy approach to visual search - i.e., directly moving to the most likely location of the target - can be suboptimal, if the target object is hard to detect. Instead it is more efficient and leads to higher detection accuracy to first look for other related objects, that are easier to detect. These provide contextual priors for the target that make it easier to find. We demonstrate this in simulation using POMDP models, focussing on two special cases: where the target object is contained within the related object, and where the target object is spatially adjacent to the related object.","PeriodicalId":304254,"journal":{"name":"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"A non-myopic approach to visual search\",\"authors\":\"Julia Vogel, Kevin P. Murphy\",\"doi\":\"10.1109/CRV.2007.5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We show how a greedy approach to visual search - i.e., directly moving to the most likely location of the target - can be suboptimal, if the target object is hard to detect. Instead it is more efficient and leads to higher detection accuracy to first look for other related objects, that are easier to detect. These provide contextual priors for the target that make it easier to find. We demonstrate this in simulation using POMDP models, focussing on two special cases: where the target object is contained within the related object, and where the target object is spatially adjacent to the related object.\",\"PeriodicalId\":304254,\"journal\":{\"name\":\"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CRV.2007.5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CRV.2007.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22

摘要

我们展示了贪婪的视觉搜索方法——即,直接移动到最有可能的目标位置——如果目标对象难以检测,可能是次优的。相反,首先寻找其他更容易检测到的相关对象,效率更高,检测精度更高。这些为目标提供了上下文先验,使其更容易被找到。我们使用POMDP模型在模拟中演示了这一点,重点关注两种特殊情况:目标对象包含在相关对象中,以及目标对象在空间上与相关对象相邻。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A non-myopic approach to visual search
We show how a greedy approach to visual search - i.e., directly moving to the most likely location of the target - can be suboptimal, if the target object is hard to detect. Instead it is more efficient and leads to higher detection accuracy to first look for other related objects, that are easier to detect. These provide contextual priors for the target that make it easier to find. We demonstrate this in simulation using POMDP models, focussing on two special cases: where the target object is contained within the related object, and where the target object is spatially adjacent to the related object.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信